train

Fit Predictive Models over Different Tuning Parameters

This function sets up a grid of tuning parameters for a number of classification and regression routines, fits each model and calculates a resampling based performance measure.

Usage

train(x, ...)
# S3 method for default
train(
  x,
  y,
  method = "rf",
  preProcess = NULL,
  ...,
  weights = NULL,
  metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
  maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE"), FALSE, TRUE),
  trControl = trainControl(),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3)
)
# S3 method for formula
train(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
# S3 method for recipe
train(
  x,
  data,
  method = "rf",
  ...,
  metric = ifelse(is.factor(y_dat), "Accuracy", "RMSE"),
  maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE"), FALSE, TRUE),
  trControl = trainControl(),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3)
)

Details

train can be used to tune models by picking the complexity parameters that are associated with the optimal resampling statistics. For particular model, a grid of parameters (if any) is created and the model is trained on slightly different data for each candidate combination of tuning parameters. Across each data set, the performance of held-out samples is calculated and the mean and standard deviation is summarized for each combination. The combination with the optimal resampling statistic is chosen as the final model and the entire training set is used to fit a final model.

The predictors in x can be most any object as long as the underlying model fit function can deal with the object class. The function was designed to work with simple matrices and data frame inputs, so some functionality may not work (e.g. pre-processing). When using string kernels, the vector of character strings should be converted to a matrix with a single column.

More details on this function can be found at http://topepo.github.io/caret/model-training-and-tuning.html.

A variety of models are currently available and are enumerated by tag (i.e. their model characteristics) at http://topepo.github.io/caret/train-models-by-tag.html.

More details on using recipes can be found at http://topepo.github.io/caret/using-recipes-with-train.html. Note that case weights can be passed into train using a role of "case weight" for a single variable. Also, if there are non-predictor columns that should be used when determining the model's performance metrics, the role of "performance var" can be used with multiple columns and these will be made available during resampling to the summaryFunction function.

References

http://topepo.github.io/caret/

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)

https://topepo.github.io/recipes/

See Also

models, trainControl, update.train, modelLookup, createFolds, recipe

Aliases

• train
• train.default
• train.formula
• train.recipe

Examples

# NOT RUN {
# }
# NOT RUN {
#######################################
## Classification Example

data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]

knnFit1 <- train(TrainData, TrainClasses,
                 method = "knn",
                 preProcess = c("center", "scale"),
                 tuneLength = 10,
                 trControl = trainControl(method = "cv"))

knnFit2 <- train(TrainData, TrainClasses,
                 method = "knn",
                 preProcess = c("center", "scale"),
                 tuneLength = 10,
                 trControl = trainControl(method = "boot"))


library(MASS)
nnetFit <- train(TrainData, TrainClasses,
                 method = "nnet",
                 preProcess = "range",
                 tuneLength = 2,
                 trace = FALSE,
                 maxit = 100)

#######################################
## Regression Example

library(mlbench)
data(BostonHousing)

lmFit <- train(medv ~ . + rm:lstat,
               data = BostonHousing,
               method = "lm")

library(rpart)
rpartFit <- train(medv ~ .,
                  data = BostonHousing,
                  method = "rpart",
                  tuneLength = 9)

#######################################
## Example with a custom metric

madSummary <- function (data,
                        lev = NULL,
                        model = NULL) {
  out <- mad(data$obs - data$pred,
             na.rm = TRUE)
  names(out) <- "MAD"
  out
}

robustControl <- trainControl(summaryFunction = madSummary)
marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2)

earthFit <- train(medv ~ .,
                  data = BostonHousing,
                  method = "earth",
                  tuneGrid = marsGrid,
                  metric = "MAD",
                  maximize = FALSE,
                  trControl = robustControl)


#######################################
## Example with a recipe

data(cox2)

cox2 <- cox2Descr
cox2$potency <- cox2IC50

library(recipes)

cox2_recipe <- recipe(potency ~ ., data = cox2) %>%
  ## Log the outcome
  step_log(potency, base = 10) %>%
  ## Remove sparse and unbalanced predictors
  step_nzv(all_predictors()) %>%
  ## Surface area predictors are highly correlated so
  ## conduct PCA just on these.
  step_pca(contains("VSA"), prefix = "surf_area_",
           threshold = .95) %>%
  ## Remove other highly correlated predictors
  step_corr(all_predictors(), -starts_with("surf_area_"),
            threshold = .90) %>%
  ## Center and scale all of the non-PCA predictors
  step_center(all_predictors(), -starts_with("surf_area_")) %>%
  step_scale(all_predictors(), -starts_with("surf_area_"))

set.seed(888)
cox2_lm <- train(cox2_recipe,
                 data = cox2,
                 method = "lm",
                 trControl = trainControl(method = "cv"))

#######################################
## Parallel Processing Example via multicore package

## library(doMC)
## registerDoMC(2)

## NOTE: don't run models form RWeka when using
### multicore. The session will crash.

## The code for train() does not change:
set.seed(1)
usingMC <-  train(medv ~ .,
                  data = BostonHousing,
                  method = "glmboost")

## or use:
## library(doMPI) or
## library(doParallel) or
## library(doSMP) and so on

# }
# NOT RUN {

# }